Redundant-Coded Masked Grid Pattern for Full-Sky Star Identification

Jiawen Liao, Xin Wei, Axi Niu, Yanning Zhang, In So Kweon, Chun Qi

Abstract: Full-sky autonomous star identification is one of the key technologies in the research on star sensors. As one of the classical pattern-based star identification methods, the grid algorithm has shown promising performance. Na further modified it to improve its robustness to position noise. However, the inherent alignment star mismatch and pattern inconsistency are still not solved. To address these problems, we propose a novel star identification method. Specifically, we design distance-guided redundant-coded patterns for different alignment stars to alleviate the problem of alignment star mismatch. Then, we create a masked grid pattern to address the inconsistency between the sensor pattern and the catalog pattern. The distances of the reference stars to their corresponding alignment stars are adopted to assist in choosing the correct alignment star, as well as reducing the number of catalog patterns that need to be evaluated. Experimental results on both synthesized and night sky images show that the proposed algorithm is quite robust to false stars, position noise, and magnitude noise. The identification accuracy of this algorithm is 98.43% with standard deviations of position noise is 2.0 pixels and 98.52% with standard deviations of magnitude noise is 0.5 Mv. Moreover, the algorithm obtains an average identification accuracy of 99.6% from night sky images.